Implementation of Energy-Efficient Auto Clustering Framework for Improving Quality of Service in IoT-enabled WSN Applications

Abstract

In today’s world, emerging Internet of Things (IoT) technology have the most potential to extend the influence of the internet through IoT-enabled devices in futuristic fields such as smart healthcare, commercial, and industrial applications. Wireless Sensor Network (WSN) is utilized for sensing and communication processes over IoT-based applications. However, the battery energy of the sensor nodes is restricted in IoT-enabled WSN (IWSN) owing to its irreplaceable ability. Most of the existing clustering schemes lagged to mitigate the control packet overhead problem since it consumes extra energy for data computation, gathering, and forwarding tasks at any environmental conditions. In this paper, a novel Energy-Efficient Auto Clustering (EEAC) framework has been proposed to develop the effective IWSN model with enhanced quality of service. The EEAC framework comprises three phases such as zone formation, node classification, and auto clustering phases. The objective of the first phase is to significantly form the different zones by alleviating the hotspot problem. Subsequently, the fuzzy logic algorithm is employed in the second phase to classify the nodes as Master, Sub-Master, and member nodes. Finally, the third phase of the proposed framework will accomplish the auto clustering mechanism based on hop information received from the reported packet. The performance results evident that the proposed EEAC framework obtains a lesser energy consumption of 0.01J during dense network and the network lifetime is prolonged up to 48% when compared with existing state-of-the-art clustering models.